CTLearn-TRN    Developed within the CTLearn frameworkhttps://github.com/ctlearn-project/ctlearn [1]}, this implementation also uses single-task approach to tackle the event reconstruction.
The network is based on an architecture called Thin-ResNet (TRN) of 34 layers. ResNet [2]} is one of the most famous and powerful architectures for classification nowadays. An attention mechanism (or content aware mechanism) called squeeze and excitation [3]} was also used: during the training it helps the network to put emphasis on the most relevant features of the image representation that is found at each layer.
The images were preprocessed with linear interpolation.
